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Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices


Affiliations
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
2 Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
3 Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
4 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
5 Krishi Vigyan Kendra, Aruppukottai 626 107, India, India
 

Crop health monitoring and assessment have become more successful with the advent of remote sensing technology in agriculture. Using this techno­logy, retrieving information about crop biophysical parameters on a non-destructive basis at spatial and temporal scales has been made possible. Several drone-derived spectral vegetation indices (VIs) have assessed crop growth status in a larger farming area. In this study, we generated VI maps for a cotton field area in the Tamil Nadu Agricultural University, Coimbatore, India. The ground-truth chlorophyll data (SPAD-502 Minolta meter) were collected from the field on the same day of drone image acquisition. Pearson correlation analysis and regression analysis were done for validation and accuracy of the ground-truth chlorophyll data and VIs. The study reveals that obtaining near real-time chlorophyll content using high spatial resolution drone images is quick and reliable

Keywords

Chlorophyll content, cotton crop, drone, multi-spectral images, spectral indices.
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  • Rani, A., Chaudhary, A., Nishant Sinha, K., Mohanty, M. and Chaudhary, R. S., Drone: the green technology for future agricul-ture. Harit Dhara, January–June 2019, 2(1).

  • Tahir, M. N., Naqvi, S. Z. A., Lan, Y., Zhang, Y., Wang, Y., Afzal, M. and Amir, S., Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard. Int. J. Precis. Agric. Aviat., 2018, 1(1).

  • Boiarskii, B. and Hasegawa, H., Technologies of cartography and field monitoring using unmanned aerial vehicles (UAV). Actual problems of agroindustrial complex: a view of young researchers.Smolensk State Agricultural Academy, 2017, pp. 213–216.

  • Maddikunta, P. K. R., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T. R., Khan, W. Z. and Pham, Q. V., Unmanned aerial vehicles in smart agriculture: applications, requirements, and chal-lenges. IEEE Sensing J., 2021, 21(16), 17608–17619.

  • Marang, I. J. et al., Machine learning optimised hyperspectral remote sensing retrieves cotton nitrogen status. Remote Sensing, 2021, 13, 1428; https://doi.org/10.3390/rs13081428.

  • Boiarskii, B. and Hasegawa, H., Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content.J. Mech. Continu. Math. Sci., 2019, 4, 20–29.

  • De Castro, A. I., Shi, Y., Maja, J. M. and Peña, J. M., UAVs for vegetation monitoring: overview and recent scientific contribu-tions. Remote Sensing, 2021, 13, 2139.

  • Zhuo, W., Wu, N., Shi, R. and Wang, Z., UAV mapping of the chlo-rophyll content in a tidal flat wetland using a combination of spec-tral and frequency indices. Remote Sensing, 2022, 14, 827.

  • Polonen, I., Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV. Proc. SPIE., 2013.

  • Wu, B., Meng, J., Li, Q., Yan, N., Du, X. and Zhang, M., Remote sensing-based global crop monitoring: experiences with China’s CropWatch system. Int. J. Digit. Earth, 2014, 7, 113–137.

  • Zheng, H. et al., Evaluation of RGB, color-infrared and multispec-tral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sensing, 2018, 10, 824.

  • Shamshiri, R. R., Mahadi, M. R., Ahmad, D., Bejo, S. K., Aziz, S.A., Ismail, W. I. W. and Che Man, H., Controller design for an osprey drone to support precision agriculture research in oil palm planta-tions. In ASABE Annual International Meeting, Washington, USA, 2017, pp. 2–13.

  • Singh, K. D., Starnes, R., Kluepfel, D. A. and Nansen, C., Qualitative analysis of walnut trees rootstock using airborne remote sensing. In Sixth Annual Plant Science Symposium, UC Davis, CA, USA, 2017, p. 1.

  • Neupane, K. and Baysal-Gurel, F., Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: a re-view. Remote Sensing, 2021, 13, 3841; https://doi.org/10.3390/ rs13193841.

  • Shaver, T. M., Kruger, G. R. and Rudnick, D. R., Crop canopy sensor orientation for late season nitrogen determination in corn. J. Plant Nutr., 2017, 40, 2217–2223.

  • Wu, Y., Li, W., Wang, Q. and Yan, S., Landslide susceptibility assess-ment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arab. J. Geosci., 2016, 9(2), 84.

  • Gitelson, A. A. et al., Relationship between gross primary produc-tion and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res. Atmos., 2006, 111.

  • Raper, T. and Varco, J., Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precis. Agric., 2015, 16, 62–76.

  • Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ.,2002, 81, 337–354.

  • Xu, X., Gu, X., Song, X., Li, C. and Huang, W., Assessing rice chlorophyll content with vegetation indices from hyperspectral data. In International Conference on Computer and Computing Technolo-gies in Agriculture, Springer, Berlin, Heidelberg, Germany, 2010, pp. 296–303.

  • Yao, X., Zhu, Y., Tian, Y., Feng, W. and Cao, W., Exploring hy-perspectral bands and estimation indices for leaf nitrogen accumu-lation in wheat. Int. J. Appl. Earth Obs. Geoinf., 2010, 12, 89–100.

  • Xia, Y. et al., Using leaf dry matter to quantify the critical nitrogen dilution curve for winter wheat cultivated in eastern China. Field Crops Res., 2014, 159, 33–42.

  • Zheng, H. et al., Evaluation of RGB, color-infrared and multispec-tral images acquired from unmanned aerial systems for the estima-tion of nitrogen accumulation in rice. Remote Sensing, 2018, 10, 824.

  • Xie, Q. et al., Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2018, 11, 1482–1493.

  • Amaral, L. R., Molin, J. P., Portz, G., Finazzi, F. B. and Cortinov, L., Comparison of crop canopy reflectance sensors used to identify sug-arcane biomass and nitrogen status. Precis. Agric., 2015, 16, 15–28.

  • Shi, Y. et al., Unmanned aerial vehicles for high-throughput pheno-typing and agronomic research. PLoS ONE, 2016, 11(7), e0159781.

  • Lu, J., Miao, Y., Huang, Y., Shi, W., Hu, X., Wang, X. and Wan, J., Evaluating an unmanned aerial vehicle-based remote sensing system for estimation of rice nitrogen status. In Proceedings of the Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics), Istanbul, Turkey, 20 July 2015, pp. 198–203.

  • Shang, J. et al., Mapping spatial variability of crop growth condi-tions using RapidEye data in Northern Ontario, Canada. Remote Sensing Environ., 2015, 168, 113–125.

  • Pandit, S., Tsuyuki, S. and Dube, T., Landscape-scale aboveground biomass estimation in buffer zone community forests of Central Nepal: coupling in situ measurements with Landsat 8 satellite data. Remote Sensing, 2018, 10, 1–18.

  • Li, M. et al., Temporal variability of precipitation and biomass of alpine grasslands on the Northern Tibetan Plateau. Remote Sensing, 2019, 11, 360.


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  • Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices

Abstract Views: 167  |  PDF Views: 89

Authors

P. Shanmugapriya
Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
K. R. Latha
Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
S. Pazhanivelan
Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
R. Kumaraperumal
Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
G. Karthikeyan
Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, India
N. S. Sudarmanian
Krishi Vigyan Kendra, Aruppukottai 626 107, India, India

Abstract


Crop health monitoring and assessment have become more successful with the advent of remote sensing technology in agriculture. Using this techno­logy, retrieving information about crop biophysical parameters on a non-destructive basis at spatial and temporal scales has been made possible. Several drone-derived spectral vegetation indices (VIs) have assessed crop growth status in a larger farming area. In this study, we generated VI maps for a cotton field area in the Tamil Nadu Agricultural University, Coimbatore, India. The ground-truth chlorophyll data (SPAD-502 Minolta meter) were collected from the field on the same day of drone image acquisition. Pearson correlation analysis and regression analysis were done for validation and accuracy of the ground-truth chlorophyll data and VIs. The study reveals that obtaining near real-time chlorophyll content using high spatial resolution drone images is quick and reliable

Keywords


Chlorophyll content, cotton crop, drone, multi-spectral images, spectral indices.

References





DOI: https://doi.org/10.18520/cs%2Fv123%2Fi12%2F1473-1480